jakezhaojb/ARAE

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jakezhaojb/ARAE

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Code for the paper "Adversarially Regularized Autoencoders (ICML 2018)" by Zhao, Kim, Zhang, Rush and LeCun

ARAE

Code for the paper "Adversarially Regularized Autoencoders (ICML 2018)" by Zhao, Kim, Zhang, Rush and LeCun https://arxiv.org/abs/1706.04223

Disclaimer

Major updates on 06/11/2018:

WGAN-GP replaced WGAN
added 1BWord dataset experiment
added Yelp transfer experiment
removed unnecessary tricks
added both RNNLM and ngram-LM evaluation for both forward and reverse PPL.

File structure

lang: ARAE for language generation, on both 1B word benchmark and SNLI
yelp: ARAE for language style transfer
mnist (in Torch): ARAE for discretized MNIST

Reference

@ARTICLE{2017arXiv170604223J,
   author = {{Junbo} and {Zhao} and {Kim}, Y. and {Zhang}, K. and {Rush}, A.~M. and 
	{LeCun}, Y.},
    title = "{Adversarially Regularized Autoencoders for Generating Discrete Structures}",
  journal = {ArXiv e-prints},
archivePrefix = "arXiv",
   eprint = {1706.04223},
 primaryClass = "cs.LG",
 keywords = {Computer Science - Learning, Computer Science - Computation and Language, Computer Science - Neural and Evolutionary Computing},
     year = 2017,
    month = jun,
   adsurl = {http://adsabs.harvard.edu/abs/2017arXiv170604223J},
  adsnote = {Provided by the SAO/NASA Astrophysics Data System}
}

Deep-Music-Autoencoder

The repo involves a Deep Learning method, Stacked Denoising Autoencoder. The repo exploits "paracel", which is a new parallel computing framework

FacialExpression

This project focuses mainly on facial expression recognition, which has been widely used in varies of fields.

Backpropagation-C

Available when training a Neural Network, or an Autoencoder.

LFW_API

For better using Labeled Faces in the Wild Benchmark, I provide the code of three versions, namely C++, python and Octave.

Online-RF-HeadPoseEstimate-cpp

This package aims at building up an Online Random Forest to train and test (can be simultaneous) a Head Pose Estimation model.

FaceDetect-Skin-YCbCr-cpp

This package implements a fast and robust Face Detection machine by C++, using the tricks of extracting skin info and transforming to YCbCr space.

FrontalFaces

This repo aims at finding the frontal faces in real time videos.

CPBD-sharpness

The code is used to evaluate the shapeness of images.

adaboost-py

A python project implementing the famous adaboost.

HSC-Classify-Pedestrian

This package implements a framework of Histograms of Sparse Coding, which validates a Pedestrian Classifier (by their different postures and directions). And a K-SVD Dictionary Learning algorithm is contributed for the training phase.

DSGA-1008-Spring2017-A1

Assignment 1 for NYU 2017 sprint class Deep Learning.

Jupyter Notebook

Delta-T-Prophet

Recommendation system for what universities you would apply.

PCA-High-Dim-Cpp

Principle Component Analysis designed specifically for High Dimensional cases.

StarAE

Advocated by Open-Source spirit and hot trend of Deep Learning, all kinds of Auto-encoder would be implemented in this repo.

toy-cnn

Toy Convolutional Neural Nets implementation by different languages.

dotfiles

.files .vim .bashrc .zshrc .tmux-tony

DSGA-1008-Spring2017-A2

DSGA-1008-Spring2017-A2

vim-torch-snipmate

torch snipmate for Vim

jz

Extra library developed for Neural Net building, based on Torch.

live_or_studio

Music Classification: Live or Studio version???

LDA-Cpp

This package implements Linear Discriminative Analysis (LDA), in C++ and with the assistance of LAPACK.

LASSO-Solver-OMP-Cpp

This package implements Orthonogal Matching Pursuit algorithm (OMP) approach as a famous LASSO solver, and this program is written in C++ assisted by LAPACK.

remeex

Machine Learning Final project

ds-ga-1008-a2

NYU course 2016 Spring 2016 Assignment 2

whatwhere

Stacked What-Where Auto-encoders

ConvAE

Convolutional Autoencoder

DSGA-1008-DeepLearning

This is the repository for assignment submission of Deep Learning course offered at NYU in Spring 2016.

Hello-NYC

Small program wrote on the day I first arriving at US.